Faculty - Dashboard

@inproceedings{bib_A_Cu_2026, AUTHOR = {Khan, Mohammed Hammad and Zope, Saurabh and Acharyya, Ishan and Srivastava, Abhishek }, TITLE = {A Current Injection Based Constant-gm Rail to Rail OTA Achieving Uniform Small And Large Signal Behaviour}, BOOKTITLE = {International Conference on VLSI Design}. YEAR = {2026}}

This paper presents a constant gm, 1.2 V operational transconductance amplifier (OTA) designed in TSMC 65 nm CMOS technology. The proposed OTA achieves a constantgm characteristic over a full rail-to-rail input/output range by employing a constant gm rail-to-rail input stage, a folded cascode summing stage, and a quiescent current controlled Class-AB output stage. The input stage comprises complementary NMOS and PMOS differential pairs connected in parallel to achieve rail-to-rail input operation. Conventional parallel input stages suffer from significant gm variation with input common-mode voltage, leading to changes in small and large signal parameters such as open-loop gain, slew rate and unity-gain bandwidth (UGB). In this work, a current-injection mechanism dynamically adjusts the active input pair’s bias, maintaining a nearly constant transconductance across the entire common-mode range. Postlayout simulation shows that the proposed OTA achieves less than 4.8% gm variation across the entire input range while maintaining uniform large signal characteristics. Performance metrics include a DC gain of 80–87 dB, UGB of 9.72 MHz, phase margin of 64°, CMRR of 97.9 dB, and a power consumption of 260 μW when driving a 100 pF capacitive load.

@inproceedings{bib_Embe_2025, AUTHOR = {Puneet, Akshat and Shankar, Pranav and Reddy, Koluguri Sri Rama Rathan and Nelson, Alan and Srikar, Somanchi and Srivastava, Abhishek }, TITLE = {Embedded AI Platform for Respiratory Auscultation in Resource Constrained Clinical Environments}, BOOKTITLE = {International Conference on Intelligent Computing and Systems at the Edge}. YEAR = {2025}}

Respiratory diseases such as asthma, bronchitis, and pneumonia remain a significant global health concern, particularly in settings where access to trained clinicians and advanced diagnostic tools is limited. This work presents an edge-based AI platform for real-time classification of respiratory conditions using lung sound analysis. The system captures respiratory audio via a microphone and processes it locally on a PYNQ-ZU FPGA platform using a lightweight Convolutional Neural Network (CNN) trained on Mel spectrogram representations. Evaluated on the publicly available ICBHI2017 dataset, the model achieved an accuracy of 96%, with a sensitivity of 97.06% and specificity of 94.12%. These results demonstrate the potential of portable, AI-driven auscultation tools to provide accessible and consistent respiratory diagnostics in resource-constrained environments.

@inproceedings{bib_Edge_2025, AUTHOR = {Reddy, Koluguri Sri Rama Rathan and Puneet, Akshat and Srivastava, Abhishek }, TITLE = {Edge-Based Object Classification Using mmWave Radar on Qualcomm Innovators Development Kit}, BOOKTITLE = {International Conference on Intelligent Computing and Systems at the Edge}. YEAR = {2025}}

Frequency Modulated Continuous Wave (FMCW) radars are emerging a reliable solution for object classification, having consistent performance under varying environmental conditions, unlike traditional Lidar and camera-based approaches. This paper presents an end-to-end object classification pipeline using point cloud data from an FMCW radar, deployed on the Qualcomm Innovators Development Kit (QIDK), demonstrating its suitability for embedded, Machine Learning and real-time interfaces. A PCA+SVM based classifier is used in the proposed system to enable efficient on-device inference with lower computational overhead. Experimental evaluation on a custom-made dataset containing over 11,000 samples shows an accuracy of 96.23%, with an inference latency of less than 25.9 milliseconds and a model size of 69.91 MB, demonstrating the suitability of the proposed approach for real-time, on-device object classification in smart mobility applications and intelligent sensing platforms.

@inproceedings{bib_"Edg_2025, AUTHOR = {Puneet, Akshat and Shankar, Pranav and Reddy, Koluguri Sri Rama Rathan and Srivastava, Abhishek }, TITLE = {"Edge-Enabled Portable Classifier for Lung Sounds Using Convolutional Neural Networks}, BOOKTITLE = {Biomedical Circuits and System Conference}. YEAR = {2025}}

Respiratory diseases such as asthma, bronchitis, and pneumonia remain a leading cause of mortality worldwide, significantly affecting both individual health and socioeconomic conditions. Early and accurate diagnosis is essential to mitigate these effects, yet traditional diagnostic techniques such as auscultation are subjective and rely on the expertise of trained physicians. To address these challenges and improve diagnostic accessibility, this work proposes a fully portable and low-cost system for the automated detection and classification of respiratory diseases. Lung sounds are captured using a microphone interfaced with a microcontroller. These recordings are then transmitted to an Edge hardware platform, where a Convolutional Neural Network (CNN) model processes Mel spectrogram representations of the audio for classification. The system leverages the publicly available HF Lung dataset for training and evaluation. The proposed system achieved a classification accuracy of 80.5%, with a sensitivity of 95.65% and a specificity of 98.80%. This portable approach demonstrates the potential for real-time, AI-powered respiratory diagnostics in remote or resource-limited settings, bridging the gap between clinical expertise and everyday health monitoring.

@inproceedings{bib_Zero_2025, AUTHOR = {Reddy, Koluguri Sri Rama Rathan and Puneet, Akshat and Srivastava, Abhishek }, TITLE = {Zero Frequency Filtering Based Portable Vital Signal Monitoring with mmWave FMCW Radars}, BOOKTITLE = {Biomedical Circuits and System Conference}. YEAR = {2025}}

Frequency Modulated Continuous Wave (FMCW) radars, known for their millimeter-level accuracy, are increasingly used in healthcare for non-contact monitoring of vital signs like Heart Rate (HR) and Breath Rate (BR). HR and BR are estimated using frequency domain analysis of the extracted respiration and cardiac activity from Radar Data. The focus of our work is to extract the waveforms of chest vibrations due to respiration and cardiac activity, for which higher time resolution is required. This work utilizes a signal processing technique known as Zero Frequency Filtering (ZFF) on the FMCW Radar Data. ZFF is known to have the best time resolution. The processing pipeline is implemented on the PYNQ-ZU platform to ensure increased portability. Experimental results show that the proposed system achieves an accuracy of over 95.26% for HR estimation and over 98.47% for BR estimation, with a latency of less than 2 ms.

@inproceedings{bib_Pred_2025, AUTHOR = {Dheer, Inesh and Mehta, Shreyas and Srikar, Somanchi and Nelson, Alan and Srivastava, Abhishek }, TITLE = {Predeployment Calibration Framework for Low-Cost Gas Sensors: An Adaptive Environmental Parameter Model}, BOOKTITLE = {IEEE Sensors Letters}. YEAR = {2025}}

Reliable toxic gas detection is vital for residential and industrial safety. While precise sensors are expensive, affordable ones face challenges of nonlinearity and environmental sensitivity, particularly from temperature (T) and humidity (H) effects. In this work, we present a novel predeployment calibration framework that accounts for these environmental factors on the sensor behavior, given by the resistance ratio (Rs/Ro) at constant gas concentration levels. The proposed method first refines the baseline resistance (Rs) estimation by fitting a power-law model to known gas concentrations and then applies a cubic regression model to capture the nonlinear effects of temperature and humidity on the Rs/Ro ratio. Cubic regression achieves superior accuracy (>5.8%) over lower order models while reducing over-fitting risks compared to higher order polynomials. It achieves 99.65% average accuracy, outperforming the 96.73% from standard libraries. This improved performance is particularly notable at low ppm levels, where direct Rs measurements are typically noisy and unstable. The enhanced stability and accuracy of the proposed method were validated over a continuous 90-min test period.

@inproceedings{bib_Theo_2025, AUTHOR = {Srivastava, Abhishek and Moudhgalya, Narahari N and Yadav, Adarsh }, TITLE = {Theory and Design of a mmWave Oscillator Utilizing high-Q Active-Mode On-Chip MEMS Resonators for Improved Fundamental Limits of Phase Noise}, BOOKTITLE = {International Symposium on VLSI Design and Test}. YEAR = {2025}}

Recent progress in MEMS resonant-fin-transistors (RFT) has enabled very high-Q active mode resonators, promising crystal-less monolithic clock generation for Millimeter-wave (mmWave) systems. However, there is a strong need for design of mmWave oscillators that utilize the high-Q of active-mode RFT (AM-RFT) optimally, while handling unique challenges such as resonator's low electromechanical transduction. In this brief, we develop a theory and through design and post-layout simulations in 14 nm Global Foundry (GF) process, we show the first active oscillator with AM-RFT at 30 GHz, which improves the fundamental limits of phase noise (PN) and figure-of-merit (FoM) when compared with the oscillators with conventional LC resonators. For AM-RFT with Q-factor of $sim$10k, post layout simulation results show that the proposed oscillator exhibits phase noise of less than -140 dBc/Hz and FoM greater than 228 dBc/Hz at a 1 MHz offset for 30 GHz center frequency, which are 25 dB better than the existing monolithic LC oscillators.

@inproceedings{bib_Desi_2025, AUTHOR = {Zope, Saurabh and Yadav, Adarsh and Moudhgalya, Narahari N and Srivastava, Abhishek }, TITLE = {Design and Implementation of novel XFMR for low-voltage mmWave-frequency VCO applications}, BOOKTITLE = {International Symposium on VLSI Design and Test}. YEAR = {2025}}

This paper presents a novel Transformer (XFMR) design intended for integrated millimeter-wave (mmWave) applications. The XFMR consists of an 8-shaped primary inductor and a dumbbell-shaped secondary inductor. This structure addresses key challenges in on-chip XFMR design by improving electromagnetic interference (EMI) suppression, reducing unwanted signal coupling, and optimizing magnetic field symmetry. Additionally, the geometry enables better layout efficiency and isolation, contributing to improved performance in tightly integrated environments. To demonstrate the practical advantages of the proposed XFMR, it is implemented in a low-voltage, low-phase-noise (PN) LC VCO designed for 23.8-27.2 GHz operation. Simulation results in TSMC 65~nm CMOS technology show a tuning range of 3.4~GHz, a PN lesser than −110 dBc/Hz at 1 MHz offset, and a figure-of-merit (FoM) greater than 186 dBc/Hz, at 0.6 V supply. These results validate the effectiveness of the XFMR in enhancing VCO performance, highlighting its potential for use in high-performance mmWave design.

@inproceedings{bib_FPGA_2025, AUTHOR = {Mohan, Anand and Meena, Hemant Kumar and Wajid, Mohd. and Srivastava, Abhishek }, TITLE = {FPGA-Based Real-Time Road Object Detection System Using mmWave Radar}, BOOKTITLE = {IEEE Embedded Systems Letters}. YEAR = {2025}}

This paper presents the development of a real-time object detection system using Frequency Modulated Continuous Wave(FMCW) millimeter-wave radar signals and the PYNQ-ZU FPGA Board, which is widely used in (advanced driving assistance system)ADAS and robotic applications. A hardware FPGA platform serves as a valid embedded architecture for the purpose of validating object detection and recognition applications. We used our experiment’s point cloud images to apply different machine learning models to detect these objects. Using a top-view (TV) filter to convert 3D point cloud images into 2D representations made object detection more accurate. Following the use of filtration techniques, we extracted features from the filtered 2D image using the VGG 16 model. We then assessed four machine learning models for object detection and found that the Support Vector Machine (SVM) model and Logistic Regression (LR) had better results, obtaining an accuracy of 97%. Our proposed work uses mmWave radar, Top-View Filter, VGG 16 Model, and LR to highly increase object detection accuracy over existing methods.

@inproceedings{bib_Anan_2025, AUTHOR = {Mohan, Anand and Meena, Hemant Kumar and Wajid, Mohd. and Srivastava, Abhishek }, TITLE = {Anand Mohan, Hemant Meena and Abhishek Srivastava, “FPGA-Based In-Vehicle Occupancy Detection Using mmWave Radar with Mexican Hat Wavelet Transform,” Accepted in , April 2025.}, BOOKTITLE = {IEEE Sensors Letters}. YEAR = {2025}}

A demonstration of the implementation of vehicle occupancy detection on hardware-software is shown in this paper. For the purpose of validating applications for vehicle occupancy detection, a hardware Field Programmable Gate Array (FPGA) platform, also known as PYNQ-ZU, is a feasible embedded architecture. Automatic in-car occupancy monitoring is an important technology in modern transportation, with major implications for safety, energy efficiency, and smart vehicle management. One of the primary benefits of mmWave radar is its ability to accurately detect the number and location of vehicle occupants, mmWave radar ensures robust detection under all lighting and weather conditions. In our research, the proposed approach was applied to point cloud images. Following the generation of 3D point cloud images, two filters, Top-View (TV), and Front-View (FV), were used to improve vehicle occupancy detection. These filters transformed 3D images into 2D ones. TV filter was found to be more effective than the FV filter. After filtering the 2D images, Mexican Hat Wavelet Transform (MHWT) was used to extract features from them. Four machine learning methods were then used to determine vehicle seat occupancy, with Logistic Regression (LR) and Support Vector Machine (SVM) producing the highest results, with an accuracy of 98%. In comparison to existing methods, the proposed approach, which utilizes mmWave radar, TV Filter, MHWT, FPGA (PYNQ-ZU), and LR, was determined to significantly improve the accuracy of vehicle occupancy detection.

@inproceedings{bib_Moti_2025, AUTHOR = {Singhal, Ashi and Wajid, Mohd. and Srivastava, Abhishek }, TITLE = {Motion Artifact Compensation in Contactless Heartbeat Monitoring Using FMCW Radar}, BOOKTITLE = {International Symposium on Circuits and Systems}. YEAR = {2025}}

The demand for contactless heartbeat monitoring systems is rapidly increasing, particularly in healthcare settings where minimizing physical contact is crucial. Conventional methods often require subjects to remain stationary to ensure accurate measurements, which restricts their use in dynamic or realworld scenarios. In this paper, we present a novel system that utilizes Texas Instruments’ 77 GHz AWR1843BOOST FMCW radar technology to circumvent this constraint. By mitigating the effects of motion artifacts, the system enables accurate heartbeat detection even when the subject is in motion. Implemented on the PYNQ-Z2 system-on-chip (SOC) platform, the system achieves an accuracy of 82.78%, providing a more robust and portable embedded solution for real-time, non-invasive heart monitoring in a dynamic environments.

@inproceedings{bib_Opti_2025, AUTHOR = {Khan, Mujeev and Wajid, Mohd. and Srivastava, Abhishek }, TITLE = {Optimized Hardware Architecture for Respiration Rate Classification using Quadratic SVM and mmWave Radar Sensor}, BOOKTITLE = {International Symposium on Circuits and Systems}. YEAR = {2025}}

In this paper, we propose an FPGA based system for non-contact monitoring of respiration rates (RR) using a mmWave frequency modulated continuous wave (FMCW) radar. The novel FPGA based hardware architecture presented in this paper classifies normal and abnormal RR with a Support Vector Machine (SVM) model using mmWave radar-based sensing. SVM with various kernels are evaluated in order to identify the most effective approach for respiratory rate (RR) classification. SVM with a quadratic polynomial kernel shows a high classification accuracy of 95%. While the accuracy was comparable to other kernels, the quadratic polynomial kernel achieved a substantial reduction in the number of support vectors and other parameters. This optimisation results in lower resource utilisation and power consumption, making it highly efficient for FPGA implementa- tion.

@inproceedings{bib_FPGA_2025, AUTHOR = {Mohan, Anand and Meena, Hemant Kumar and Wajid, Mohd. and Srivastava, Abhishek }, TITLE = {FPGA-Based Real-Time Road Object Detection System Using mmWave Radar}, BOOKTITLE = {IEEE Sensors Letters}. YEAR = {2025}}

This letter presents the development of a real-time object detection system using frequency modulated continuous wave millimeter-wave (mmWave) radar signals and the python productivity for zynq ultrascale + mpsocs (PYNQ-ZU) field-programmable gate array (FPGA) board, which is widely used in advanced driving assistance system and robotic applications. A hardware FPGA platform serves as a valid embedded architecture for the purpose of validating object detection and recognition applications. We used our experiment’s point cloud images to apply different machine learning models to detect these objects. Using a top-view (TV) filter to convert 3-D point cloud images into 2-D representations made object detection more accurate. Following the use of filtration techniques, we extracted features from the filtered 2-D image using the visual geometry group (VGG) 16 model. We then assessed four machine learning models for object detection and found that the support vector machine (SVM) model and logistic regression (LR) had better results, obtaining an accuracy of 97%. Our proposed work uses mmWave radar, TV filter, VGG 16 Model, and LR to highly increase object detection accuracy over existing methods.

@inproceedings{bib_Desi_2025, AUTHOR = {S, Abhinav and Acharyya, Ishan and Khetan, Umesh and Wajid, Mohd and Srivastava, Abhishek }, TITLE = {Design of Delta Sigma Modulator Using Approximate Adder With Near-Normal Error Distribution For Fractional-N Frequency Synthesizer}, BOOKTITLE = {International Symposium on Circuits and Systems}. YEAR = {2025}}

This paper presents a novel approach to the design of Delta-Sigma Modulators (DSMs) for Fractional-N Phase Locked Loop (PLL) Frequency Synthesizers by incorporating an approximate adder characterized by near-normal error distribution. Traditional Frequency Synthesizers utilize DSMs that rely on precise adders and Pseudo-Random Binary Sequence (PRBS) generators to provide modulus inputs to multi-modulus frequency dividers for achieving Fractional-N Frequency Synthesis. The inclusion of PRBS is critical in introducing randomness in the DSM's output, thereby mitigating frequency spurs in the output of the synthesizer. In contrast, the proposed DSM design leverages an approximate adder with near-normal error distribution to introduce the required randomness, eliminating the dependency on PRBS. This innovative design leads to substantial improvements in power efficiency, area, processing speed and improves the performance of the synthesizer. The proposed design demonstrates a reduction of approximately 44.7% in power consumption and 39.73% in area compared to conventional DSMs combined with PRBS generators when implemented in 65nm TSMC technology. The proposed design achieves comparable accuracy in output frequency, maintains similar Signal-to-Noise Ratio (SNR) and Effective Number of Bits (ENOB), and shows improvement in Spurious-Free Dynamic Range (SFDR).

@inproceedings{bib_Anal_2024, AUTHOR = {Harikrishna, Kambham and RAMPRASAD, MEERA and Srivastava, Abhishek }, TITLE = {Analysis and Measurement of Electrically Small Surface Mount Inductor as Radiating Element for WBAN Communication in MedRadio Band}, BOOKTITLE = {Biomedical Circuits and System Conference}. YEAR = {2024}}

Wireless body area network (WBAN) communica- tions in 400 MHz Medical Device Radio-communication (MedRa- dio) band seek wearable and implantable sensor nodes with extremely small antennas for reduced form factor. However, it is very challenging to shrink the 400 MHz antenna dimensions to sub-cm region for the conventional 50 Ω transmitters (TX) due to large wavelength. Thus there is a strong need to investigate extremely small, unconventional radiating elements with alternate TX topologies. In this work, we utilize extremely small surface mount inductors (SML) as a radiating element in non-50 Ω MedRadio band TX and present detailed fundamental analysis and models for its radiating-zone boundaries and corresponding path losses. To validate the proposed theory, simulation and measurement results of path loss of a cylindrical SML (1.2mm × 8.5mm) have been also presented, which show congruence with analytical models within ≤ 3 dB.

@inproceedings{bib_VLSI_2024, AUTHOR = {Khan, Mujeev and Singhal, Ashi and Wajid, Mohd and Srivastava, Abhishek }, TITLE = {VLSI Architecture for the Phase Unwrapping Module in Contactless Vibration Sensing for Biomedical Applications}, BOOKTITLE = {IEEE International Symposium on Smart Electronic Systems}. YEAR = {2024}}

Phase-unwrapping (PU) is a critical signal processing step used in fields such as radar, sonar, and laser technology. This paper presents a faster and more efficient VLSI architecture for implementing the phase-unwrapping algorithm on FPGA platforms. To evaluate the effectiveness of the proposed FPGAbased VLSI architecture for the phase-unwrapping algorithm in chest wall vibration sensing, we compared its performance with both a PYNQ board implementation and an equivalent software solution. The results are compared in terms of speed, resource utilization, and power consumption. Additionally, prototypes were developed on both the ZedBoard and PYNQ board, and the outcomes were physically verified. The proposed VLSI architecture achieves nearly 70× and 111× speed-up compared to software-based solutions and PYNQ board implementations, respectively.

@inproceedings{bib_Auto_2024, AUTHOR = {Mohan, Anand and Meena, Hemant Kumar and Srivastava, Abhishek }, TITLE = {Automatic In-Vehicle Occupancy Detection Using Low Cost Mm-Wave Radar}, BOOKTITLE = {International Conference on Robotics and Automation}. YEAR = {2024}}

These days, autonomous vehicles are equipped with a variety of sensors that allow them to sense their environment and control several functions. In particular, if a child is left alone in a parked car, the passenger occupancy detection system can detect it. Traditionally, camera sensors have been used to detect vehicle occupancy. Cameras perform well in bright light but struggle in low light. To address these limitations, we opted to detect vehicle occupancy using millimeter-wave radar which operates accurately regardless of lighting conditions. Figure 1 shows the pipeline of the proposed algorithm. Figure 2 depicts the setup for Vehicle occupancy Detection with FMCW radar, specifically the Texas Instruments AWR1843 booster pack evaluation board, which operates in the 76-81 GHz band. Figure 3 depicts vehicle occupancy detection images.

@inproceedings{bib_Scal_2024, AUTHOR = {Benny, Jewel and Moudhgalya, Narahari N and Khan, Mujeev and Meena, Hemant Kumar and Wajid, Mohd and Srivastava, Abhishek }, TITLE = {Scalable Multi-Subject Vital Sign Monitoring with mmWave FMCW Radar and FPGA Prototyping}, BOOKTITLE = {IEEE Sensors Journal}. YEAR = {2024}}

In this work, we introduce an innovative approach to estimate the vital signs of multiple human subjects simultaneously in a non-contact way using a Frequency Modulated Continuous Wave (FMCW) radar-based system. Traditional vital sign monitoring methods often face significant limitations, including subject discomfort with wearable devices, challenges in calibration, and the risk of infection transmission through contact measurement devices. To address these issues, this research is motivated by the need for versatile, non-contact vital monitoring solutions applicable in various critical scenarios. This work also explores the challenges of extending this capability to an arbitrary number of subjects, including hardware and theoretical limitations. Supported by rigorous experimental results and discussions, the paper illustrates the system’s potential to redefine vital sign monitoring. An FPGA-based implementation is also presented as proof of concept for a hardware-based and portable solution, improving upon previous works by offering 2.7x faster execution and 18.4% less Look-Up Table (LUT) utilization, as well as providing over 7400x acceleration compared to its software counterpart.

@inproceedings{bib_Hard_2024, AUTHOR = {Singh, Yash Pratap and Gupta, Aham and Chaudhary, Devansh and Mahajan, Pranjal and Wajid, Mohd. and Srivastava, Abhishek }, TITLE = {Hardware Deployable Edge AI Solution for Posture Classification using mmWave Radar and Low Computation Machine Learning Model}, BOOKTITLE = {IEEE Sensors Journal}. YEAR = {2024}}

Identifying correct human postures is crucial in areas like patient care in hospitals. However, the traditional vision-based methods widely used for this purpose raise privacy concerns for the subject, and the other wearable sensor-based approaches are impractical for real-world scenarios. In this paper, we propose a contactless, privacy-conscious, and memoryefficient posture classification system based on millimeter wave (mmWave) radar. This system utilizes threedimension(3D) point-cloud data captured using Texas Instrument’s IWR1843BOOST Frequency Modulated Continuous Wave (FMCW) radar module to classify the posture of the subject. Two types of datasets are extracted from this radar data: (i) image dataset derived from the isometric view of the point-cloud data, and (ii) spatial coordinates dataset also extracted from the point-cloud data. A low-computational Tiny Machine Learning (TinyML) model is employed on the datasets for efficient implementation on embedded hardware, Raspberry Pi 3 B+. The proposed model’s parameters were quantized to 8 bits (int8), which accurately classify four postures, i.e., standing, sitting, lying, and bending, with an accuracy of 98.97% for the image data. However, to make it more computationally efficient, the int8 quantized TinyML model was trained on the spatial coordinates dataset, giving an accuracy of 96.12%. This highlights the efficiency and effectiveness of our proposed lightweight model that can be deployed on edge devices for real-world applications.

@inproceedings{bib_Gold_2024, AUTHOR = {Tripathi, Anushka and Sahni, Arpit and Srikar, Somanchi and Sresthavadhani, Mantha Venkata Surya and Khan, Mohammed Hammad and Srivastava, Abhishek }, TITLE = {GoldAid: IoT-Powered Integrated Safety System with CNN for Rapid Medical Aid in Golden Hour}, BOOKTITLE = {IEEE Sensors Journal}. YEAR = {2024}}

@inproceedings{bib_A_Po_2024, AUTHOR = {Mahajan, Pranjal and Chaudhary, Devansh and Khan, Mujeev and Khan, Mohammed Hammad and Wajid, Mohd and Srivastava, Abhishek }, TITLE = {A Point Cloud-Based Non-Intrusive Approach for Human Posture Classification by Utilizing 77 GHz FMCW Radar and Deep Learning Models}, BOOKTITLE = {IEEE International Symposium on Circuits and Systems}. YEAR = {2024}}

Human posture analysis has gained a significant research interest in the recent times. It helps in many applications such as gait analysis for detecting neurological disorders, fall detection of elderly people, and continuous monitoring of severely ill patients. Camera-based vision systems are commonly employed for detecting human postures; however, they cause concerns over the subjects' privacy. To address this challenge, we present a millimeter wave (mmWave) radar-based, truly non-contact, non-intrusive, and privacy-conscious posture detection and classification system in this research. The proposed system utilizes three-dimensional point cloud data of the subject to comprehensively classify body postures, capturing intricate real-time details. In this work, we also present a custom-designed Convolutional Neural Network (CNN) and its comparison with other models, which are conventionally used for posture classification. We also demonstrate the hardware implementation of the proposed system and present the measurement results using Texas Instruments' IWR1843BOOST radar module. The proposed CNN model achieves an accuracy of 97.10% while classifying standing, sitting, lying and bending postures.

@inproceedings{bib_Desi_2024, AUTHOR = {Khan, Mujeev and Mahajan, Pranjal and Khan, Gani Nawaz and Chaudhary, Devansh and Benny, Jewel and Wajid, Mohd. and Srivastava, Abhishek }, TITLE = {Design and Implementation of FPGA Based System for Object Detection and Range Estimation Used in ADAS Applications Utilizing FMCW Radar}, BOOKTITLE = {IEEE International Symposium on Circuits and Systems}. YEAR = {2024}}

This paper presents the design and implementation of a hardware system for real-time object detection and range estimation utilizing Frequency-Modulated Continuous Wave (FMCW) millimeter wave radar signals, which are commonly used in Advance Driver Assistance Systems (ADAS) and robotic applications. The proposed system utilizes the Fast Fourier Transform (FFT) algorithm to process the FMCW radar signal for estimating the range of an object. For developing a resource-efficient and low latency range-estimation hardware, this paper also presents a comparative analysis for the implementation of three popular FFT architectures (iterative Radix-2, iterative Radix-4, and pipelined Radix-2) on FPGA. Based on the presented analysis the lowest latency range-estimation architecture has been chosen for FFT implementation and a system prototype is developed as a proof-of-concept by integrating the commercially available Texas Instruments' 77 GHz AWR1642BOOST FMCW radar module with a FPGA to host the chosen FFT architecture. Measurement results of the proposed system hardware are also presented in this paper, which shows >99.21% range-estimation accuracy.

@inproceedings{bib_Desi_2023, AUTHOR = {Sahni, Arpit and Varma, Kosuri Vikranth and Deeksha, and Ghosh, Bhaswar and Sarje, Anshu and Srivastava, Abhishek }, TITLE = {Design of Integrated System for Detection of Micro-organisms with Fabrication and Testing of ZnO Nanorods based Biosensor}, BOOKTITLE = {Biomedical Circuits and System Conference}. YEAR = {2023}}

Efficient and reliable non-invasive methods for micro-organism detection have significant implications in various fields such as medical diagnostics, environmental monitoring, and food safety. Existing detection techniques, such as culture-based methods, polymerase chain reaction (PCR) have limitations including time-consuming processes, expensive equipment requirements and invasive sampling procedures. These drawbacks emphasize the need for alternate detection techniques that are non-invasive, rapid, sensitive, specific, and user-friendly. For this purpose, in this research, we present a portable integrated system for micro-organism detection, which is based on electrochemical impedance spectroscopy (EIS). The proposed system includes Zinc Oxide (ZnO) nanorods based interdigitized electrode (IDE) sensor and an EIS circuit. This work also presents the considerations and methodology for the fabrication of ZnO nanorods with high-yield. To validate the efficacy of the proposed methods, experimental results through scanning electron microscope are also presented. Finally, measurement results for detection of a micro-organism (yeast) is also presented to demonstrate the utility and effectiveness of the proposed methods and system.

@inproceedings{bib_Desi_2023, AUTHOR = {Sahni, Arpit and Srivastava, Abhishek }, TITLE = {Design of a Wideband 8-20 GHz Receiver Front-End with Reduced Local Oscillator Phase-Error in 4-Path Mixer}, BOOKTITLE = {Asia Pacific Conference on Circuits and Systems}. YEAR = {2023}}

Modern transceiver integrated circuits used in wireless communication systems seek low power wide frequency range receiver front-ends (RxFE) to support multiple communication bands. For this, RxFE leveraging passive N-path mixer first approach are becoming progressively popular due to their low power nature. However, N-path RxFE requires N precise phases of a local oscillator (LO) to control mixer operation, which becomes challenging as the frequency of operation increases (> 10 GHz). In this paper, we give useful insights and present design considerations for the faithful generation of different non- overlapping phases of LO to achieve wide band RF input match. We also present the design and post-layout simulation results of a 4-path mixer based 8-20 GHz RxFE with programmable gain of 10 to 29 dB in TSMC 65 nm CMOS technology. In post-layout simulations, the proposed design achieves an IIP3 of > -10 dB, S11 of < -12 dB and consumes total power of 66 mW from 1.2 V supply.

@inproceedings{bib_Deep_2023, AUTHOR = {Edakkadan, Adithya Sunil and Srivastava, Abhishek }, TITLE = {Deep Learning Based Portable Respiratory Sound Classification System}, BOOKTITLE = {Asia Pacific Conference on Circuits and Systems}. YEAR = {2023}}

Respiratory diseases contribute to a majority of deaths worldwide every year. Diseases such as asthma, bronchitis and pneumonia also adversely impact a person’s social and economic conditions. They can seriously threaten their health if left undiagnosed and untreated. Techniques such as auscultation are used in the diagnosis of most respiratory diseases. However, using such techniques requires an experienced physician and the diagnosis is subjective. To overcome these challenges, in this work, a portable handheld system has been proposed and a proof of concept implemented to detect and classify respiratory diseases automatically through the use of convolutional neural networks (CNN) running on mobile platforms. Mel spectrograms are generated from the audio signals and fed to the CNN for classification. This work makes use of the publicly available HF Lung dataset. The classification accuracy achieved by the current implementation using a Raspberry Pi for processing is 80.55% with a sensitivity of 95.65% and specificity of 98.80% on the HF Lung dataset.

@inproceedings{bib_An_m_2023, AUTHOR = {Sresthavadhani, Mantha Venkata Surya and Edakkadan, Adithya Sunil and Sahni, Arpit and Srivastava, Abhishek }, TITLE = {An mmWave Frequency Range Multi-Modulus Programmable Divider for FMCW Radar Applications}, BOOKTITLE = {International Conference on VLSI Design}. YEAR = {2023}}

Recently, there has been a major shift in advanced driver assistance systems (ADAS) from the camera and LiDAR-oriented systems to mmWave radar-based systems. Such high-frequency band radars currently using frequency modulated continuous wave (FMCW) technique require programmable dividers with large divide ratios and fine frequency resolution to obtain high-frequency chirps with sufficiently large bandwidth. This paper presents a low-power multi-modulus programmable frequency divider for a frequency synthesizer operating in the 19.25-20.25 GHz frequency band from a reference frequency of 40 MHz. The divider consumes 14 mW of power and is capable of producing divide ratios in the range of 481.25-506.25 with a fine resolution of 1 MHz. The divider has a phase noise of −146 dBc/Hz at an offset of 2 kHz.

@inproceedings{bib_Anal_2023, AUTHOR = {Harikrishna, Kambham and Chatterjee, Srayan Sankar and Edakkadan, Adithya Sunil and Srivastava, Abhishek }, TITLE = {Analysis and Design of Low Phase Noise 20 GHz VCO for Frequency Modulated Continuous Wave Chirp Synthesizers in mmWave Radars}, BOOKTITLE = {International Conference on VLSI Design}. YEAR = {2023}}

Recent developments in advanced driver assistance systems (ADAS) have raised the demands of mmWave radars in 24 GHz and 77 GHz band. For higher accuracy and precision, frequency modulated continuous wave (FMCW) technique has become very popular for mmWave radars, which requires low phase noise and high bandwidth chirp frequency synthesizers. In this work, we present analysis and design of a low phase noise, transformer tank based mmWave voltage controlled oscillator (VCO) near 20 GHz for multiplier based 77 GHz FMCW chirp synthesizer. The proposed VCO design is implemented in 65 nm CMOS technology. Post layout simulations show that the proposed VCO exhibits a figure-of-merit is 192 dBc/Hz and phase noise of -117.98 dBc/Hz at 1 MHz offset while operating at 18.94 GHz and achieves a tuning range of 1.42 GHz (18.94-20.36 GHz), while consuming a power of 13.78 mW from 1.1 V supply.

@inproceedings{bib_Desi_2023, AUTHOR = {Chatterjee, Srayan Sankar and Sahni, Arpit and Harikrishna, Kambham and Abbas, Zia and Srivastava, Abhishek }, TITLE = {Design and Analysis of Low Power 20 GHz Colpitts VCO with FoM of 196.26 dBc/Hz}, BOOKTITLE = {International Midwest Symposium on Circuits and Systems}. YEAR = {2023}}

This work introduces an ultra low power gm-boosted Colpitts voltage controlled oscillator (VCO) with a transformer based tank operating near 20 GHz. The proposed VCO is implemented in TSMC 65 nm CMOS technology. Through postlayout simulations, it is demonstrated that the proposed VCO exhibits a remarkable figure-of-merit of 196.26 dBc/Hz and achieves a phase noise of -111.21 dBc/Hz at a 1 MHz offset while operating at 19.6 GHz. Additionally, the VCO provides a tuning range of 1.7 GHz (18 - 19.7 GHz) and consumes only 1.2 mW of power from a 1 V supply.

@inproceedings{bib_Anal_2023, AUTHOR = {Chatterjee, Srayan Sankar and Harikrishna, Kambham and Edakkadan, Adithya Sunil and Srivastava, Abhishek }, TITLE = {Analysis and Design of Low Phase Noise 20 GHz VCO for Frequency Modulated Continuous Wave Chirp Synthesizers in mmWave Radars}, BOOKTITLE = {International Conference on VLSI Design}. YEAR = {2023}}

Recent developments in advanced driver assistance systems (ADAS) have raised the demands of mmWave radars in 24 GHz and 77 GHz band. For higher accuracy and precision, frequency modulated continuous wave (FMCW) technique has become very popular for mmWave radars, which requires low phase noise and high bandwidth chirp frequency synthesizers. In this work, we present analysis and design of a low phase noise, transformer tank based mmWave voltage controlled oscillator (VCO) near 20 GHz for multiplier based 77 GHz FMCW chirp synthesizer. The proposed VCO design is implemented in 65 nm CMOS technology. Post layout simulations show that the proposed VCO exhibits a figure-of-merit is 192 dBc/Hz and phase noise of -117.98 dBc/Hz at 1 MHz offset while operating at 18.94 GHz and achieves a tuning range of 1.42 GHz (18.94-20.36 GHz), while consuming a power of 13.78 mW from 1.1 V supply.

@inproceedings{bib_Desi_2023, AUTHOR = {Benny, Jewel and Mahajan, Pranjal and Chatterjee, Srayan Sankar and Wajid, Mohd and Srivastava, Abhishek }, TITLE = {Design and Measurements of mmWave FMCW Radar Based Non-Contact Multi-Patient Heart Rate and Breath Rate Monitoring System}, BOOKTITLE = {Biomedical Circuits and System Conference}. YEAR = {2023}}

Recent developments in mmWave radar technologies have enabled the truly non-contact heart-rate (HR) and breath-rate (BR) measurement approaches, which provides a great ease in patient monitoring. Additionally, these technologies also provide opportunities to simultaneously detect HR and BR of multiple patients, which has become increasingly important for efficient mass monitoring scenarios. In this work, a frequency modulated continuous wave (FMCW) mmWave radar based truly non-contact multiple patient HR and BR monitoring system has been presented. Furthermore, a novel approach is also proposed, which combines multiple processing methods using a least squares solution to improve measurement accuracy, generalization, and handle measurement error. The proposed system has been developed using Texas Instruments’ FMCW radar and experimental results with multiple subjects are also presented, which show >97% and >93% accuracy in the measured BR and HR values, respectively.

@inproceedings{bib_A_2._2022, AUTHOR = {Edakkadan, Adithya Sunil and Desai, Kuntal and Srivastava, Abhishek }, TITLE = {A 2.75-2.94 GHz Voltage Controlled Oscillator with Low Gain Variation for Quantum Sensing Applications}, BOOKTITLE = {International Conference on VLSI Design}. YEAR = {2022}}

Quantum sensing applications such as nitrogen vacancy based magnetometry require phase locked loops (PLL), which can synthesize microwave frequencies in a narrow band near 2.87 GHz. Moreover, it is also required that the PLLs should have low noise and low jitter for high stability and fast settling time. These requirements seek low phase noise voltage controlled oscillator (VCO) with small variation in its gain (KVCO) within the desired tuning range. In this paper, we present a technique for designing a low phase noise VCO with low KVCO and small KVCO variation. To validate the proposed technique, an LC VCO has been designed and implemented in 0.18 µm CMOS process and post layout simulation results are presented. The simulation results show that the proposed LC VCO achieves KVCO variation of ±3.82% in the frequency range of 2.75 - 2.94 GHz and exhibits a phase noise of -118.64 dBc/Hz at an offset of 1 MHz, while consuming 9 mW of power from a 1.8 V supply.

@inproceedings{bib_A_Lo_2022, AUTHOR = {Srivastava, Abhishek and Chatterjee, Baibhab and Weinstein, Dana and Sen, Shreyas }, TITLE = {A Low Phase Noise 30 GHz Oscillator Topology for Resonant-Fin-Transistors Based High-Q On-chip Resonators in 14 nm Technology}, BOOKTITLE = {International Conference on VLSI Design}. YEAR = {2022}}

This work presents an ultra low phase noise 30 GHz oscillator topology in 14 nm technology for an active mode Micro-electro-mechanical systems (MEMS) based resonator that utilizes resonant-fin transistors (RFT). A novel oscillator architecture has been presented for the active mode RFT, which can be modelled as a voltage controlled current source with 270° phase shift between its output current and input voltage. The proposed oscillator has been designed in 14 nm GF technology and simulation results show that it achieves phase noise less than −144 dBc/Hz at 1 MHz offset for 30 GHz carrier frequency for active mode RFT with quality factor of 10,000, while consuming 5.5 mW power from 0.8 V supply.

@inproceedings{bib_Desi_2022, AUTHOR = {Papreja, Ashish and Sresthavadhani, Mantha Venkata Surya and Srivastava, Abhishek }, TITLE = {Design Methodology of Low Phase Noise mmWave Oscillator with Partial Cancellation of Static Capacitance of High-Q On-chip MEMS Resonator}, BOOKTITLE = {International Conference on VLSI Design}. YEAR = {2022}}

Fully monolithic carrier synthesis at mmWave frequencies (>10 GHz) seek on-chip high quality factor resonators in micro-electromechanical systems (MEMS) technology. However, at mmWave frequencies, MEMS resonator’s usage is severely limited by its high static capacitance (C 0 ) and low electromechanical coupling. In this work, we propose a technique of partial cancellation of C 0 of mmWave MEMS resonator to utilize it for the development of an extremely low phase noise oscillator. Based on the proposed technique, we also present a methodology for low phase noise fully monolithic mmWave oscillator design. To validate the proposed technique and methodology, design and simulation results of an oscillator with 10.33 GHz on-chip MEMS resonator model in 65 nm CMOS have been also presented in this paper. Simulation results show that the oscillator achieves phase noise and FoM of -137 dBc/Hz and 212.8 dBc/Hz, respectively at 1 MHz offset, while consuming 2.8 mW power from 1 V supply.

@inproceedings{bib_Desi_2022, AUTHOR = {Edakkadan, Adithya Sunil and Saha, Kasturi and Baghini, Maryam Shojaei and Srivastava, Abhishek }, TITLE = {Design of 2.87 GHz Frequency Synthesizer with Programmable Sweep for Diamond Color Defect based CMOS Quantum Sensing Applications}, BOOKTITLE = {IEEE International Symposium on Circuits and Systems}. YEAR = {2022}}

Recently, diamond color defect based quantum sensing applications such as nitrogen-vacancy (NV) center magnetometry have emerged in CMOS technology, which use optically detected magnetic resonance (ODMR) for sensing magnetic field strengths (|B~|) from different environmental physical quantities. For ODMR based sensing, CMOS quantum sensors seek an onchip 2.87 GHz microwave (MW) signal generator. Moreover, in order to sense smaller |B~|, these CMOS quantum sensors also require that MW signal should be swept with sufficiently small step-size near 2.87 GHz. In this work, we present a fractional-N synthesizer based 2.87 GHz MW-generator (MWG) with an extremely small programmable sweep step-size for improved sensitivity of |B~| measurements in CMOS NV magnetometry. The proposed MWG is implemented in 180 nm CMOS technology and simulations were done to validate the proposed design. Post-layout simulation results show that the proposed MWG achieves a minimum sweep-step size of 50 kHz, which can be used to sense |B~|<0.9μT and exhibits a phase noise of −114.5 dBc/Hz at an offset of 1 MHz near 2.87 GHz center frequency.

@inproceedings{bib_Desi_2022, AUTHOR = {Srivastava, Abhishek and Das, Devarshi and Baghini, Maryam Shojaei }, TITLE = {Design and implementation of 0.23 nJ/bit reference-spur-free FSK/OOK transmitter at 400 MHz for wearable health monitoring}, BOOKTITLE = {Biomedical Circuits and System Conference}. YEAR = {2022}}

Increasing demands of wearable health monitoring seek dedicated spectrum for short range wireless communication of biosignals, for which frequency band near 400 MHz has been utilized recently. While using the dedicated spectrum, biosignal communication must not cause any out of band radiations and interference, for which there is a strong need to develop spur- free, low phase noise and energy efficient wireless transmitters (TX). In this research, for the first time we present design and implementation of a reference-spur-free, low phase noise, sub-mW dual modulation mode (frequency shift keying (FSK) and on-off keying (OOK)) 400 MHz TX for wearable health monitoring, where RF synthesis depends solely on an extremely low phase noise oscillator, which uses a 400 MHz surface acoustic wave resonator and does not include any power hungry, spur- generating frequency multiplier or phase/frequency lock loop. The proposed TX has been designed and fabricated in 180 nm CMOS technology. Measurement results show that the energy efficiencies of the proposed TX in FSK and OOK mode are 1.8 nJ/bit at 250 kb/s and 0.23 nJ/bit at 2 Mb/s, respectively. To prove the utility of the proposed TX, biosignal communication is also demonstrated with the help of a commercial receiver in actual operating scenarios, where the measured bit error rate in FSK mode was < 10−5 for range of 2 m. Index Terms—Health-monitoring, wearable, spur-free, trans- mitter, FSK, OOK, 400 MHz, MedRadio

@inproceedings{bib_Anal_2021, AUTHOR = {Srivastava, Abhishek and , Baibhab Chatterjee and Rawat, Udit and He, Yanbo and Weinstein, Dana and Sen, Shreyas }, TITLE = {Analysis and Design Considerations for Achieving the Fundamental Limits of Phase Noise in mmWave Oscillators With On-Chip MEMS Resonator}, BOOKTITLE = {IEEE Transactions on Circuits and Systems II}. YEAR = {2021}}

Very small electromechanical coupling coefficient in micro-electromechanical systems (MEMS) or acoustic resonators is quite a concern for oscillator performance, specially at mmWave frequencies. This small coefficient is the manifestation of the small ratio of motional capacitance to static capacitance in the resonators. This work provides a general solution to overcome the problem of relatively high static capacitance at mmWave frequencies and presents analysis and design techniques for achieving extremely low phase noise and a very high figureof-merit (FoM) in an on-chip MEMS resonator based mmWave oscillator. The proposed analysis and techniques are validated with design and simulation of a 30 GHz oscillator with MEMS resonator having quality factor of 10,000 in 14 nm GF technology. Post layout simulation results show that it achieves a phase noise of −132 dBc/Hz and FoM of 217 dBc/Hz at offset of 1 MHz.

@inproceedings{bib_Desi_2021, AUTHOR = {Srivastava, Abhishek and Das, Devarshi and Baghini, Maryam Shojaei }, TITLE = {Design and Implementation of 0.23 nJ/Bit Reference-Spur-Free FSK/OOK Transmitter at 400 MHz forWearable Health Monitoring}, BOOKTITLE = {Biomedical Circuits and System Conference}. YEAR = {2021}}

Increasing demands of wearable health monitoring seek dedicated spectrum for short range wireless communication of biosignals, for which frequency band near 400 MHz has been utilized recently. While using the dedicated spectrum, biosignal communication must not cause any out of band radiations and interference, for which there is a strong need to develop spurfree, low phase noise and energy efficient wireless transmitters (TX). In this research, for the first time we present design and implementation of a reference-spur-free, low phase noise, sub-mW dual modulation mode (frequency shift keying (FSK) and on-off keying (OOK)) 400 MHz TX for wearable health monitoring, where RF synthesis depends solely on an extremely low phase noise oscillator, which uses a 400 MHz surface acoustic wave resonator and does not include any power hungry, spurgenerating frequency multiplier or phase/frequency lock loop. The proposed TX has been designed and fabricated in 180 nm CMOS technology. Measurement results show that the energy efficiencies of the proposed TX in FSK and OOK mode are 1.8 nJ/bit at 250 kb/s and 0.23 nJ/bit at 2 Mb/s, respectively. To prove the utility of the proposed TX, biosignal communication is also demonstrated with the help of a commercial receiver in actual operating scenarios, where the measured bit error rate in FSK mode was < 10−5 for range of 2 m. Index Terms—Health-monitoring, wearable, spur-free, transmitter, FSK, OOK, 400 MHz, MedRadio

@inproceedings{bib_Anal_2019, AUTHOR = {Srivastava, Abhishek and Baghini, Maryam Shojaei }, TITLE = {Analysis and Design of Low Phase Noise LC Oscillator for Sub-mW PLL-Free Biomedical Receivers}, BOOKTITLE = {International Conference on VLSI Design}. YEAR = {2019}}

This work presents the design of a low phase noise LC oscillator (LCO) for a phase locked loop (PLL)-free receiver (RX), which is used for wireless sensor nodes based healthcare applications. A detailed analysis of phase noise of free running LCO has been presented under the influence of offchip components, which are very often inevitable in biomedical radios. Based on the analysis, a low power cross coupled LCO has been optimally designed for Medical Device Radiocommunication (MedRadio) band, which is a recommended communication band for biomedical applications. The proposed LCO has been fabricated in 180 nm CMOS technology. Measurement results show that the LCO consumes 140 μW power from 1.8 V supply and exhibits a phase noise of -103 dBc/Hz at an offset of 300 kHz, which closely matches with the post layout simulation results. Measurement results also show that the proposed …

PhD Students (4)

MS Students (10)

Dual Degree Students (3)

Honours Students (7)